The kernels considered here are positive semi-definite and obey in addition \(\mathbf {K}\sqrt{\mathbf {f}}=\mathbf {0}_n\) . We call them standard kernels. They can be related to the weighted version of centered kernels of Machine Learning [1]}. To each weighted configuration \((\mathbf {f},\mathbf {D})\)  corresponds a unique standard kernel \(\mathbf {K}\) , and conversely.
